问题描述
我的实验表明，微软VC ++ 6编译器中的随机数生成器
是一个统计RNG，其显着性水平为
。
SL> 1.0％（例如1.001％）的统计测试通过测试，
但1.0％未通过...
有人可以确认这个发现吗？
各种软件产品的RNG功能可以用其显着性水平进行分析和分类
如上图所示。
我认为这个IMO的重要发现值得深入研究...... :-)
对于测试方法，请参阅：
My experiments show that the random number generator
in Microsoft''s VC++6 compiler is a statistical RNG with a
significance level 1.0%.
Statistical testing at SL >1.0% (for example 1.001%) passes the test,
but 1.0% does not pass...
Can anybody confirm this finding?
The RNG function of the various SW products can be
analyzed and classified better using its significance level
as shown above.
I think this IMO important finding deserves a deeper research... :-)
For the testing method see:
http://en.wikipedia.org/wiki/Binomial_test
http://en.wikipedia.org/wiki/Binomial_distribution
推荐答案
您对样本量和零假设使用了什么？
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What are you using for your sample size and null hypothesis?
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你在为样本使用什么大小和零假设？
What are you using for your sample size and null hypothesis?
详情请参阅：
样本大小为500（即调用rand（）500次），
rnd range是37（即0到36;是的，轮盘模拟）。
在每次rand（）调用后执行上面提到的StatTest。
上面说的在一个循环中调用超过30次，每次初始化频率统计数据时
。
srand（time（0））在pgmstart只执行一次。
Here the details:
Sample size is 500 (ie. calling rand() 500 times),
rnd range is 37 (ie. 0 to 36; yes, a roulette simulation).
Doing the above mentioned StatTest after each rand() call.
The above said is called more than 30 times in a loop,
each time initializing the freq stats anew.
srand(time(0)) done only once at pgmstart.
您对样本量和零假设使用了什么？
What are you using for your sample size and null hypothesis?
H0 = RNG仅在显着性水平> 1％
即通过随机性测试。例如尝试1％和1.001％，你将会看到它总是在< = 1％的水平上失败并且总是在> 1％sig水平上通过。
H0 = RNG passes the randomness test only at a significance level >1%
ie. try out 1% and 1.001% for example and you will
see it always fails at the <=1% level and always passes at >1% sig level.
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